Optimization algorithms have been utilized to be able to optimize processes in which a large amount of data is processed from sensors which sense various of the measurable quantities within a monitored system. This large amount of data has been utilized in the past to derive an initial optimization algorithm which is then used to optimize a monitored system.
Optimization algorithms are those that can include both diagnostics and prognostications. One such system involving the PRDICTR algorithm is described in U.S. patent application Ser. No. 12/548,683 by Carolyn Spier filed on Aug. 27, 2009, assigned to the assignee hereof and incorporated herein by reference.
When however it is important to embed an optimization algorithm at a point of performance such as on a vehicle, due to the large amount of data originally required for providing robust optimization, there needs to be a way of reducing the amount of sensor data required in order to derive an initial optimization algorithm, so that the optimization algorithm can be placed at the point of performance.
Thus, there is a need to create an optimization algorithm that one can embed at the point of performance. The normal method of creating such an algorithm is precluded because of the necessity of collecting a large amount of data and then finding a vector through the data that has the highest coefficient of goodness of fit. For vehicle embedded systems, this approach is impractical. Moreover, vehicle health monitoring systems invariably have small numbers of monitored parameters in which only a few variables on vehicles are monitored.
Note that vehicles exhibit a number of different failure modes and monitored conditions can include the number of hours of operation, a number of interactions, and numbers of specific environments that are encountered by the vehicle. However, if the monitored data is a small data set, then oftentimes it is difficult to justify a complete optimization algorithm study such as that produced by the aforementioned PRDICTR algorithm.
Note that if one could wait to collect data on all of the failure modes of a vehicle and then find the performance vector that suggests how the system is going to fail in a new environment, then it would be possible to derive the appropriate optimization algorithm.
However, when the amount of data is limited and one cannot interpolate a vector through the modest amount of data, there needs to be a way of synthetically generating data in order to be able to derive an initial optimization algorithm.